An image‐guidance system for dynamic dose calculation in prostate brachytherapy using ultrasound and fluoroscopy
Bibliographic record
Abstract
PURPOSE: Brachytherapy is a standard option of care for prostate cancer patients but may be improved by dynamic dose calculation based on localized seed positions. The American Brachytherapy Society states that the major current limitation of intraoperative treatment planning is the inability to localize the seeds in relation to the prostate. An image-guidance system was therefore developed to localize seeds for dynamic dose calculation. METHODS: The proposed system is based on transrectal ultrasound (TRUS) and mobile C-arm fluoroscopy, while using a simple fiducial with seed-like markers to compute pose from the nonencoded C-arm. Three or more fluoroscopic images and an ultrasound volume are acquired and processed by a pipeline of algorithms: (1) seed segmentation, (2) fiducial detection with pose estimation, (3) seed matching with reconstruction, and (4) fluoroscopy-to-TRUS registration. RESULTS: The system was evaluated on ten phantom cases, resulting in an overall mean error of 1.3 mm. The system was also tested on 37 patients and each algorithm was evaluated. Seed segmentation resulted in a 1% false negative rate and 2% false positive rate. Fiducial detection with pose estimation resulted in a 98% detection rate. Seed matching with reconstruction had a mean error of 0.4 mm. Fluoroscopy-to-TRUS registration had a mean error of 1.3 mm. Moreover, a comparison of dose calculations between the authors' intraoperative method and an independent postoperative method shows a small difference of 7% and 2% forD90 and V100, respectively. Finally, the system demonstrated the ability to detect cold spots and required a total processing time of approximately 1 min. CONCLUSIONS: The proposed image-guidance system is the first practical approach to dynamic dose calculation, outperforming earlier solutions in terms of robustness, ease of use, and functional completeness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".